Speed Distribution by Pokémon Type

Electric and Flying types tend to be fastest, while Rock types are slowest

This visualization explores the speed distribution across different Pokémon types using data from the {pokemon} R package. The beeswarm plot reveals distinct patterns in how speed stats vary between types, with Electric and Flying Pokémon showcasing the highest average speeds while Rock types tend to be the slowest. Diamond markers indicate mean values for each type, providing insight into competitive gameplay advantages.

Figure 1: A beeswarm plot showing speed distribution across Pokémon types. Types are ordered by average speed, with Flying and Electric types at the bottom (fastest) and Rock near the top (slowest). Purple diamonds mark the mean speed for each type, and a vertical dashed line at 100 indicates the “Fast” threshold. Most Pokémon fall between 50-100 speed, with outliers reaching up to 150.

Steps to Create this Graphic

1. Load Packages & Setup

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    ggbeeswarm,     # Categorical Scatter (Violin Point) Plots 
    camcorder       # Record Your Plot History 
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  7,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

tt <- tidytuesdayR::tt_load(2025, week = 13) 

pokemon_raw <- tt$pokemon_df |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

glimpse(pokemon_raw)
skim(pokemon_raw)

4. Tidy Data

### |-  tidy data ----
pokemon_clean <- pokemon_raw |>
    select(pokemon, type_1, speed, generation_id, color_1) |>
    filter(!is.na(speed), !is.na(type_1)) |>
    mutate(generation = as.factor(generation_id))

# Summarize speed stats 
ordered_types <- pokemon_clean |>
    group_by(type_1) |>
    summarize(
        across(
            speed,
            list(
                mean = ~mean(.x, na.rm = TRUE),
                median = ~median(.x, na.rm = TRUE),
                min = ~min(.x, na.rm = TRUE),
                max = ~max(.x, na.rm = TRUE)
            ),
            .names = "{fn}_{.col}"
        ),
        count = n(),
        .groups = "drop"
    ) |>
    arrange(desc(mean_speed)) |>
    pull(type_1)

# plot data
pokemon_clean <- pokemon_clean |>
    mutate(type_1 = factor(type_1, levels = ordered_types))

5. Visualization Parameters

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(rep("#7570B3", 18))
)

### |-  titles and caption ----
title_text <- str_glue("Speed Distribution by Pokémon Type")
subtitle_text <- str_glue("Electric and Flying types tend to be fastest, while Rock types are slowest")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 13,
    source_text =  "{ pokemon R package }" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray80", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray80", linewidth = 0.02),
        
        # Legend elements
        legend.position = "plot",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

### |-  Plot  ----
p <- pokemon_clean |>
    ggplot(aes(x = type_1, y = speed, color = type_1)) +
    # Geoms
    geom_beeswarm(cex = 1, size = 1.2, alpha = 0.75, shape = 21, na.rm = TRUE) +
    stat_summary(   # mean indicator
        fun = mean, 
        geom = "point", 
        shape = 18, 
        size = 3, 
        color =  alpha(colors$palette[1], 0.9),
    ) +
    geom_hline(
        yintercept = 100, 
        linetype = "dashed",
        color = "darkgray", 
        ) +
    # Annotate
    annotate(
        "text", x = length(ordered_types), y = 102, 
        label = "Fast (Speed ≥ 100)", hjust = 0, vjust = -2,
        size = 3, color = "dimgray"
        ) +
    annotate(
        "point",  
        x = "steel", 
        y = 140, 
        shape = 18, 
        size = 4, 
        color = alpha(colors$palette[1], 0.9)
    ) +
    annotate(
        "text",
        x = "steel", y = 141, label = "  = Mean",
        color =  alpha(colors$palette[1], 0.9),
        size = 4.2,
        angle = 0,
        hjust = 0,
        fontface = "bold"
    ) +
    
    # Scales
    scale_color_manual(values = colors$palette) +
    coord_flip() +
    # labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = NULL,
        y = "Base Speed Stat"
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(2),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.85),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    ) 

7. Save

### |-  plot image ----  
save_plot_patchwork(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 13, 
  width = 7,
  height = 10
)

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0  ggbeeswarm_0.7.2 here_1.0.1       glue_1.8.0      
 [5] scales_1.3.0     skimr_2.1.5      janitor_2.2.0    showtext_0.9-7  
 [9] showtextdb_3.0   sysfonts_0.8.9   ggtext_0.1.2     lubridate_1.9.3 
[13] forcats_1.0.0    stringr_1.5.1    dplyr_1.1.4      purrr_1.0.2     
[17] readr_2.1.5      tidyr_1.3.1      tibble_3.2.1     ggplot2_3.5.1   
[21] tidyverse_2.0.0  pacman_0.5.1    

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   vipor_0.4.7        farver_2.1.2       fastmap_1.2.0     
 [5] gh_1.4.1           digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4       
[13] tools_4.4.0        utf8_1.2.4         yaml_2.3.10        knitr_1.49        
[17] labeling_0.4.3     htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0        
[21] xml2_1.3.6         repr_1.1.7         tidytuesdayR_1.1.2 withr_3.0.2       
[25] grid_4.4.0         fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2    
[29] cli_3.6.3          rmarkdown_2.29     crayon_1.5.3       generics_0.1.3    
[33] rstudioapi_0.17.1  tzdb_0.4.0         commonmark_1.9.2   parallel_4.4.0    
[37] ggplotify_0.1.2    base64enc_0.1-3    vctrs_0.6.5        yulab.utils_0.1.8 
[41] jsonlite_1.8.9     gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2       
[45] beeswarm_0.4.0     systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[49] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[53] pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1          
[57] httr2_1.0.6        rprojroot_2.0.4    vroom_1.6.5        evaluate_1.0.1    
[61] markdown_1.13      gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[65] Rcpp_1.0.13-1      svglite_2.1.3      xfun_0.49          fs_1.6.5          
[69] pkgconfig_2.0.3   

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_13.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Sources:

   • TidyTuesday 2025 Week 13: Pokemon